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@article{195415,
        author = {C. K. Joe Uzuegbu and P. J. Ezigbo and E. S. Mbonu and V. O Aniugo and C. C. Ogomaka and A. T. Umerah and A. G. Imoke},
        title = {Simulation-Based Evaluation of Energy-Efficient Power Electronics Systems Using Wide-Bandgap Devices, AI Control Algorithms, and IoT Optimization in the Nigerian Grid Context},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5130-5138},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195415},
        abstract = {This study examines whether three connected modernization layers—wide-bandgap power devices, artificial-intelligence-based control, and Internet-of-Things-enabled monitoring—can improve power-electronic performance under Nigerian grid conditions. To answer that question, the paper develops a five-model simulation framework using MATLAB/Simulink, Simscape Electrical, Python, Pandapower, pandas, and scikit-learn. The models cover five practical problems: silicon-versus-silicon-carbide inverter efficiency, ANN-based maximum power point tracking for photovoltaic systems, feeder-level load forecasting, AI-assisted harmonic control in EV charging, and voltage recovery after a transformer-side fault in a Nigerian-style five-bus network. Model inputs were drawn from the cited literature and adjusted to represent tropical operating conditions and weak-grid constraints. Across the five studies, the silicon-carbide inverter maintained higher efficiency over the 1–5 kW load range, with an average gain of 5.4 percentage points over the silicon benchmark. The ANN-based MPPT controller reduced convergence time from 1.78 s to 1.21 s, improved tracking accuracy from 92.6% to 97.1%, and increased harvested energy from 212.4 Wh to 224.8 Wh. The IoT-enabled forecasting model achieved 94.0% prediction accuracy with a mean absolute error of 18.6 kW. In the EV charging model, AI-assisted switching reduced total harmonic distortion from 12.8% to 3.2% while raising power factor from 0.91 to 0.99. In the grid-fault model, AI-assisted compensation restored bus voltage to 0.96 pu within 30 ms, whereas the uncompensated case recovered much more slowly. Taken together, these results show that better converter hardware, smarter control, and real-time visibility can improve efficiency, renewable integration, power quality, and disturbance recovery in the Nigerian power sector.},
        keywords = {wide bandgap devices; Silicon Carbide; MATLAB/Simulink; Pandapower; AI control; IoT optimization; Nigerian grid; MPPT; harmonics; fault recovery},
        month = {April},
        }